Content based News Recommendation via Shortest Entity Distance over Knowledge Graphs
This addresses the challenge of recommending news articles without user-specific information, particularly in cold-start scenarios, though it is incremental as it builds on existing knowledge graph approaches.
The paper tackles the cold-start problem in content-based news recommendation by proposing a graph traversal algorithm that computes the shortest distance between named entities over a knowledge graph, resulting in stronger Pearson correlation to human similarity scores than other methods.
Content-based news recommendation systems need to recommend news articles based on the topics and content of articles without using user specific information. Many news articles describe the occurrence of specific events and named entities including people, places or objects. In this paper, we propose a graph traversal algorithm as well as a novel weighting scheme for cold-start content based news recommendation utilizing these named entities. Seeking to create a higher degree of user-specific relevance, our algorithm computes the shortest distance between named entities, across news articles, over a large knowledge graph. Moreover, we have created a new human annotated data set for evaluating content based news recommendation systems. Experimental results show our method is suitable to tackle the hard cold-start problem and it produces stronger Pearson correlation to human similarity scores than other cold-start methods. Our method is also complementary and a combination with the conventional cold-start recommendation methods may yield significant performance gains. The dataset, CNRec, is available at: https://github.com/kevinj22/CNRec